Ultra-low permeability reservoir refracturing well selection and layer selection method based on geological engineering integration

Abstract

According to the ultra-low permeability reservoir refracturing well and layer selection method based on geological engineering integration, geological factors, fracturing factors and production factors are comprehensively considered, the grey relational analysis method, the analytic hierarchy process, the fuzzy clustering analysis method and the quadrant analysis method are comprehensively utilized, the optimal refracturing well layer is optimized, the cost is reduced, and the working efficiency is improved. The method is favorable for optimizing the design of a fracturing transformation scheme, improves the success rate and the effective rate of re-fracturing, is high in applicability, and provides important technical support for the yield improvement of ultra-low permeability reservoir fracturing transformation. A data statistical analysis and grey correlation degree analysis method is used for preliminary screening, influence factors with the small correlation degree with the initial monthly oil production after refracturing are removed in advance, the optimal refracturing well layer is preferably selected on the basis of main control influence factors, and ultralow permeability reservoir refracturing well selection and layer selection based on geological engineering integration are achieved.

Description

Technical Field

[0001] The invention belongs to the technical field of oil and natural gas development, and in particular relates to a method for selecting wells and layers for repeated fracturing of ultra-low permeability reservoirs based on geological and engineering integration. Background Art

[0002] With the widespread application of hydraulic fracturing technology in ultra-low permeability oil and gas fields, reasonable fracturing well selection and layer selection are the key points of ultra-low permeability reservoir fracturing transformation research. After the initial fracturing production of ultra-low permeability reservoirs, the single well production gradually decreases, and repeated fracturing technology is urgently needed to achieve the effect of fracturing transformation and production increase, and reasonable well layer selection is the prerequisite for the success of repeated fracturing measures.

[0003] The methods for selecting wells and layers for repeated fracturing include: First, the production statistics method, which uses production statistical data analysis to quickly screen each well and determine that wells with poor performance need to be re-fractured. The disadvantage of this method is that when selecting suitable wells, the initial screening is not comprehensive enough, and it only targets wells with poor performance, not all wells. It is suitable for oil fields with good reservoir heterogeneity. Second, the composition recognition method, which selects re-fracturing wells through fuzzy recognition, membership theory, and the Euclidean closeness of each well to the ideal well for repeated fracturing. This method fails to reflect the difference in the influence of each main control factor on the effect of repeated fracturing. The third is the fracturing well selection and layer selection method based on the artificial neural network algorithm. Its training process has high requirements for field data and requires a large amount of production, fracturing construction and other data of repeated fracturing wells. Fourth, based on the dynamic and static data after the initial fracturing, mathematical statistical methods are used to make repeated fracturing decisions on the premise of determining the influence of various factors on the effect of repeated fracturing.

[0004] In the existing research, CN201910572781.4 published a process-based fracturing well selection method, and formed a complete set of fracturing well selection methods based on the database system; CN202011631108.2 published a fracturing acidizing well selection method based on grey correlation method and hierarchical analysis method. This method uses grey correlation method and hierarchical analysis method to realize fracturing acidizing well selection, which changes the expert's experience in weighting the well characteristic data in the traditional well selection method, and the calculation is simple and practical; CN202011525340.8 authorized a method and system for offshore low permeability gas field fracturing technology well selection, which solves the problem that the existing well selection method for terrestrial gas field fracturing technology has a low fracturing economic threshold and a low lower limit of reservoir physical properties; CN202010753148.8 published a method for selecting layers and selecting fracturing methods for repeated fracturing of horizontal wells, which solves the problem that the fracturing methods in the prior art are non-targeted and costly. The shortcomings of the above invention are that it does not comprehensively analyze and screen the constraints on well and layer selection for repeated fracturing, omits geological characteristic parameters, and the applicability of the well and layer selection method is general. Summary of the invention

[0005] The purpose of the present invention is to provide a method for selecting wells and layers for repeated fracturing of ultra-low permeability reservoirs based on geological and engineering integration, so as to overcome the above-mentioned technical problems existing in the prior art.

[0006] To this end, the technical solution provided by the present invention is as follows:

[0007] The method for selecting wells and layers for repeated fracturing of ultra-low permeability reservoirs based on geological and engineering integration includes the following steps:

[0008] Step 1) establishing original data sets of geological factors, fracturing factors and production factors;

[0009] Step 2) According to the established original data set, the original data set is standardized through range transformation, and then the correlation and weight value of the influencing factors are determined by using the grey correlation analysis method to screen the main control influencing parameters;

[0010] Step 3) Use the analytic hierarchy process to construct a multi-parameter evaluation system hierarchy and determine the weight values ​​of the main control influencing parameters;

[0011] Step 4) constructing a fuzzy comprehensive evaluation matrix based on geological factors, fracturing factors and production factors according to the fuzzy clustering method, combining the weight values ​​of the main control influencing parameters, and calculating the comprehensive weight coefficient value using the fuzzy decision model;

[0012] Step 5) taking the comprehensive weight coefficient value as the re-fracturing quality classification threshold; and using the quadrant analysis method to draw different re-fracturing effect areas according to the re-fracturing quality classification threshold and the initial monthly oil production of fracturing;

[0013] Step 6) The second quadrant of II is selected first, followed by the third quadrant of III and the first quadrant of I, and the well layer to be repeatedly fractured is determined.

[0014] The geological factors in step 1) include porosity, permeability, oil saturation, effective thickness, length of oil-bearing sandstone, and remaining recoverable reserves.

[0015] The fracturing factors in step 1) include fracturability, fracture half-length, proppant dosage, fracturing fluid dosage, sand addition amount, sand ratio, and displacement.

[0016] The production factors in step 1) include formation pressure, water content, production pressure difference, recovery degree, and initial monthly oil production after fracturing.

[0017] The specific process of screening the main control influencing parameters in step 2) is as follows:

[0018] S1. Standardization of the original data of well and layer selection for repeated fracturing:

[0019] The range transformation method is used to normalize the data into dimensionless values, and the parameters are standardized into positive or negative indicators:

[0020] Positive indicators:

[0021]

[0022] Negative indicators:

[0023]

[0024] Where: S is the normalized value of the original data, dimensionless, and the value is between 0 and 1; X is the original data value; X min is the minimum value of the original data; X max is the maximum value of the original data;

[0025] S2. Construct reference sequence and comparison sequence, and determine the correlation degree according to the grey correlation analysis method:

[0026] The target is to increase the production of ultra-low permeability reservoir by fracturing. The original data set is the comparison sequence X i ={X i (k)|k=1,2,···,m|}(i=1,2,···,n), the initial monthly oil production of fracturing is the reference sequence X o ={X o (k)|k=1,2,···,m|}, determine the correlation coefficient between the comparison sequence and the reference sequence of different influencing factors;

[0027]

[0028] Among them, δ i (k) is the correlation between the comparison sequence and the reference sequence under different influencing factors; ρ is the resolution coefficient, ρ∈[0,1], the smaller the ρ value, the higher the resolution of the correlation degree, and the value range of ρ here is 0.4-0.6; |X o (k)-X i (k)| is the absolute difference between the reference sequence and the kth index in the i-th sequence; is the minimum absolute value of the sequence index; is the maximum absolute value of the sequence index; k is the number of wells, k = 1, 2, ..., m; i is the number of influencing factors, i = 1, 2, ..., n;

[0029] S3. Determine the weight of the influencing factors according to the correlation, and select the main influencing parameters of the fracturing production increase effect:

[0030]

[0031] Calculate the correlation using the mean method:

[0032]

[0033] Among them, C i is the weight value of influencing factor i; r i is the correlation of influencing factor i;

[0034] According to the weight values ​​of the influencing factors, the influencing factors with weight values ​​< 0.05 are removed, and the influencing factors with weight values ​​>= 0.05 are determined as the main control influencing parameters.

[0035] The specific process of determining the weight value of the main control influencing parameter in step 3) is as follows:

[0036] S1. Use the analytic hierarchy process to construct a hierarchical structure diagram for selecting wells and layers for repeated fracturing:

[0037] The hierarchical structure diagram of well and layer selection for repeated fracturing includes target layer, criterion layer and scheme layer, with well and layer selection for repeated fracturing as the target layer, main control influencing parameters as the criterion layer and single well as the scheme layer;

[0038] S2. According to the weight values ​​of the main control influencing parameters determined by the grey correlation analysis method, a judgment matrix A is constructed. ij ) n×n , a in the judgment matrix ij Indicates the importance of master control influencing parameter i relative to master control influencing parameter j, a ji Indicates the importance of the main control influencing parameter j relative to the main control influencing parameter i, and the importance of the main control influencing parameter is determined using the 1-9 scaling method;

[0039] S3, use the geometric mean method to process the weight vector and maximum eigenvalue of the judgment matrix constructed in S2:

[0040] Process the row vectors of the judgment matrix:

[0041]

[0042] Will Normalization processing:

[0043]

[0044] Determine the weight of each influencing parameter of this layer on the target layer:

[0045] W=[W 1 ,W 2 ,···,W n ] T

[0046] Where: W 1 , W 2 ,···,W nare the weight values ​​of different influencing parameters respectively; n is the number of parameters;

[0047] The maximum eigenvalue of the judgment matrix is:

[0048]

[0049] S4. Perform consistency check on the judgment matrix:

[0050]

[0051]

[0052] Among them, CI is the consistency index of the judgment matrix; CR is the random consistency ratio; RI is the consistency index; when CR < 0.10, the consistency of the judgment matrix is ​​considered reasonable; conversely, when CR ≥ 0.10, the judgment matrix needs to be appropriately adjusted until CR < 0.10.

[0053] The specific process of calculating the comprehensive weight coefficient value using the fuzzy decision model in step 4) is as follows:

[0054] S1. Constructing the evaluation unit matrix of repeated fracturing well selection and layer selection R = (r ij ) n×m , and converted into a fuzzy comprehensive evaluation matrix B = (b ij ) n×m , n is the main influencing factor of the evaluation, and m is the evaluation well layer;

[0055] S2. Establish a fuzzy decision model and calculate the comprehensive weight coefficient value:

[0056] T=W×B

[0057] T=[t 1 ,t 2 ,···,t m ] T ,

[0058] Among them, t i is the comprehensive weight coefficient value of each single well.

[0059] In S1, the evaluation unit matrix R = (r ij ) n×m Converted into fuzzy comprehensive evaluation matrix B = (b ij ) n×m When , the larger the better the influencing factors are, the following relationship is used for conversion:

[0060]

[0061] For the influencing factors that are smaller, the better, they are converted through the following relationship:

[0062]

[0063] In the formula, (r ij ) max represents the maximum value of the i-th influencing factor in the m-th sample; (r ij ) min Indicates the minimum value of the i-th influencing factor in the n-th sample.

[0064] The beneficial effects of the present invention are:

[0065] The well and layer selection method of the present invention comprehensively considers geological factors, fracturing factors and production factors, and comprehensively utilizes grey correlation analysis, hierarchical analysis method, fuzzy cluster analysis method and quadrant analysis method to select the best repeated fracturing well layer, reduce costs, and is conducive to optimizing the design of fracturing transformation schemes, thereby improving the success rate and efficiency of repeated fracturing, having high applicability, and providing important technical support for fracturing transformation and production increase in ultra-low permeability oil reservoirs.

[0066] The present invention utilizes data statistical analysis and grey correlation analysis to conduct preliminary screening, removes in advance the influencing factors with a small correlation with the initial monthly oil production after repeated fracturing, focuses on the main controlling influencing factors, selects the best well layers for repeated fracturing, and realizes the well and layer selection for repeated fracturing of ultra-low permeability oil reservoirs based on the integration of geology and engineering.

[0067] The present invention constructs a multi-parameter evaluation system hierarchy through the hierarchical analysis method, which can comprehensively compare the weight values ​​of quantitative parameters at different levels, and has the advantage of directly clarifying the main controlling factors of repeated fracturing. The fuzzy comprehensive evaluation matrix is ​​constructed by the fuzzy clustering method, which can effectively detect and extract key factors and determine their comprehensive weight coefficient values, and has the advantages of accuracy and efficiency. Through the quadrant analysis method, different repeated fracturing effect areas can be obtained, which has the advantage of intelligent division and optimization of the best repeated fracturing potential area. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 is a flow chart of the method of the present invention;

[0069] Figure 2 A schematic diagram of the hierarchical structure of well and layer selection for repeated fracturing constructed by the analytic hierarchy process in the present invention;

[0070] Figure 3 A schematic diagram of the refracturing effect area divided by the quadrant analysis method of the present invention;

[0071] Figure 4 It is a schematic diagram of the repeated fracturing effect area divided by the quadrant analysis method in Example 2. DETAILED DESCRIPTION

[0072] The following describes the implementation of the present invention through specific embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification.

[0073] The exemplary embodiments of the present invention are now described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention.

[0074] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0075] Example 1

[0076] The present invention provides a method for selecting wells and layers for repeated fracturing of ultra-low permeability reservoirs based on geological and engineering integration. Figure 1 As shown, the following steps are included:

[0077] Step 1) establishing original data sets of geological factors, fracturing factors and production factors;

[0078] Step 2) According to the established original data set, the original data set is standardized through range transformation, and then the correlation and weight value of the influencing factors are determined by using the grey correlation analysis method to screen the main control influencing parameters;

[0079] Step 3) Use the analytic hierarchy process to construct a multi-parameter evaluation system hierarchy and determine the weight values ​​of the main control influencing parameters;

[0080] Step 4) constructing a fuzzy comprehensive evaluation matrix based on geological factors, fracturing factors and production factors according to the fuzzy clustering method, combining the weight values ​​of the main control influencing parameters, and calculating the comprehensive weight coefficient value using the fuzzy decision model;

[0081] Step 5) taking the comprehensive weight coefficient value as the re-fracturing quality classification threshold; and using the quadrant analysis method to draw different re-fracturing effect areas according to the re-fracturing quality classification threshold and the initial monthly oil production of fracturing;

[0082] Step 6) The second quadrant of II is selected first, followed by the third quadrant of III and the first quadrant of I, and the well layer to be repeatedly fractured is determined.

[0083] The well and layer selection method of the present invention comprehensively considers geological factors, fracturing factors and production factors, and comprehensively utilizes grey correlation analysis, hierarchical analysis method, fuzzy cluster analysis method and quadrant analysis method to select the best repeated fracturing well layer, reduce costs, and is conducive to optimizing the design of fracturing transformation schemes, thereby improving the success rate and efficiency of repeated fracturing, having high applicability, and providing important technical support for fracturing transformation and production increase in ultra-low permeability oil reservoirs.

[0084] Example 2

[0085] In order to further illustrate the method of the present invention, based on Example 1, this example takes three wells WJ1, WJ2 and WJ3 in XX oil field as examples to perform repeated fracturing and well selection. The specific process is as follows:

[0086] Step 1) establishing original data sets of geological factors, fracturing factors and production factors;

[0087] Oil and water well fracturing well and layer selection criteria:

[0088] (1) Standards for selecting wells and layers for fracturing: The fracturing layer should have sufficient remaining recoverable reserves and formation energy; wells with good production conditions before fracturing and low cumulative production; wells with low production or reduced production due to pollution of low permeability production layers; wells with improved water injection conditions and good connectivity between oil and water wells should be fractured to induce efficiency; reservoirs with exploratory well tests or oil test results that do not match drilling, logging, and well logging interpretation data should decide whether to perform fracturing based on reservoir requirements; comprehensive consideration of factors such as the difference in ground stress of the fracturing reservoir and the distance from the upper and lower water layers or gas layers; oil layers that have obtained low-yield oil flow through conventional tests and can obtain industrial oil flow after fracturing after evaluation; water injection well sections that do not meet the injection requirements and have poor fluid absorption.

[0089] (2) Establish the original data set based on the well and layer selection criteria for oil and water well fracturing:

[0090] like Figure 2 As shown, the original data set includes geological parameter data, fracturing parameter data and production parameter data. The geological parameters include porosity, permeability, oil saturation, effective thickness, oil-bearing sandstone length, and remaining recoverable reserves; the fracturing parameters include fracturing ability, fracture half-length, proppant dosage, fracturing fluid dosage, sand addition amount, sand ratio, and displacement; the production parameters include formation pressure, water content, production pressure difference, bottom hole flowing pressure, production degree, and initial monthly oil production of fracturing.

[0091] Influencing factors of the three wells:

[0092]

[0093] Step 2) Based on the established original data set, the grey correlation analysis method is used to determine the correlation and weight values ​​of the influencing factors, and the main control influencing parameters are screened;

[0094] S1. Standardization of the original data of well and layer selection for repeated fracturing:

[0095] The range transformation method is used to normalize the data into dimensionless values, and the parameters are standardized into positive or negative indicators:

[0096] Positive indicators:

[0097]

[0098] Negative indicators:

[0099]

[0100] Where: S is the normalized value of the original data, dimensionless, and the value is between 0 and 1; X is the original data value; X min is the minimum value of the original data; X max is the maximum value of the original data;

[0101] S2. Construct reference sequence and comparison sequence, and determine the correlation degree according to the grey correlation analysis method:

[0102] The target is to increase the production of ultra-low permeability reservoir by fracturing. The original data set is the comparison sequence X i ={X i (k)|k=1,2,···,m|}(i=1,2,···,n), the initial monthly oil production of fracturing is the reference sequence X o ={X o (k)|k=1,2,···,m|}, determine the correlation coefficient between the comparison sequence and the reference sequence of different influencing factors;

[0103]

[0104] Among them, δ i (k) is the correlation between the comparison sequence and the reference sequence under different influencing factors; ρ is the resolution coefficient, ρ∈[0,1], the smaller the ρ value, the higher the resolution of the correlation degree, and the value range of ρ here is 0.4-0.6; |X o (k)-X i (k)| is the absolute difference between the reference sequence and the kth index in the i-th sequence; is the minimum absolute value of the sequence index; is the maximum absolute value of the sequence index; k is the number of wells, k = 1, 2, ..., m; i is the number of influencing factors, i = 1, 2, ..., n;

[0105] S3. Determine the weight of the influencing factors according to the correlation, and select the main influencing parameters of the fracturing production increase effect:

[0106]

[0107] Calculate the correlation using the mean method:

[0108]

[0109] Among them, C i is the weight value of influencing factor i; r i is the correlation of influencing factor i;

[0110] According to the weight values ​​of the influencing factors, the influencing factors with weight values ​​< 0.05 are removed, and the influencing factors with weight values ​​>= 0.05 are determined as the main control influencing parameters.

[0111] The correlation and weight of influencing factors:

[0112]

[0113] The main influencing parameters screened are: permeability, oil saturation, effective thickness, remaining recoverable reserves, fracture half-length, fracturing fluid dosage, sand addition amount, displacement, production pressure difference, and recovery degree.

[0114] Step 3) Use the analytic hierarchy process to construct a multi-parameter evaluation system hierarchy and determine the weight values ​​of the main control influencing parameters;

[0115] S1. Use the analytic hierarchy process to construct a hierarchical structure diagram for selecting wells and layers for repeated fracturing:

[0116] The hierarchical structure diagram of well and layer selection for repeated fracturing includes target layer, criterion layer and scheme layer, with well and layer selection for repeated fracturing as the target layer, main control influencing parameters as the criterion layer and single well as the scheme layer;

[0117] S2. Construct a judgment matrix based on the main control influencing parameters after standardization;

[0118] According to the weight values ​​of the main control influencing parameters determined by the grey correlation analysis method, the judgment matrix A is constructed. ij ) n×n , a in the judgment matrix ij Indicates the importance of master control influencing parameter i relative to master control influencing parameter j, a ji Indicates the importance of the main control influencing parameter j relative to the main control influencing parameter i, and the importance of the main control influencing parameter is determined using the 1-9 scaling method;

[0119] S3, use the geometric mean method to process the weight vector and maximum eigenvalue of the judgment matrix constructed in S3:

[0120] Process the row vectors of the judgment matrix:

[0121]

[0122] Will Normalization processing:

[0123]

[0124] Determine the weight of each influencing parameter of this layer on the target layer:

[0125] W=[W 1 ,W 2 ,···,W n ] T

[0126] Where: W 1 , W 2 ,···,W n are the weight values ​​of different influencing parameters respectively; n is the number of parameters;

[0127] The maximum eigenvalue of the judgment matrix is:

[0128]

[0129] S5. Perform consistency check on the judgment matrix:

[0130]

[0131]

[0132] Among them, CI is the consistency index of the judgment matrix; CR is the random consistency ratio; RI is the consistency index; when CR < 0.10, the consistency of the judgment matrix is ​​considered reasonable; conversely, when CR ≥ 0.10, the judgment matrix needs to be appropriately adjusted until CR < 0.10.

[0133] The purpose of this step is to reduce human factors and reliance on experience in view of the complex environmental risks of low permeability oil and gas reservoir exploitation, and to make the results more referenceable. The judgment matrix needs to be constructed to meet the consistency principle and pass the consistency test.

[0134] Judgment matrix:

[0135]

[0136]

[0137] W=[0.0204,0.0613,0.1482,0.0613,0.0409,0.1226,0.0816,0.1632,0.1839,0.1022] T, the maximum eigenvalue of the judgment matrix is ​​calculated to be 10.0467. At this time, n = 10, RI = 1.49, CI = 0.0052, CR = 0.0035, that is, CR < 0.10, and the consistency of the judgment matrix is ​​reasonable.

[0138] Step 4) constructing a fuzzy comprehensive evaluation matrix based on geological factors, fracturing factors and production factors according to the fuzzy clustering method, combining the weight values ​​of the main control influencing parameters, and calculating the comprehensive weight coefficient value using the fuzzy decision model;

[0139] S1. Constructing the evaluation unit matrix of repeated fracturing well selection and layer selection R = (r ij ) n×m , and converted into a fuzzy comprehensive evaluation matrix B = (b ij ) n×m , n is the main influencing factor of the evaluation, and m is the evaluation well layer;

[0140] For the influencing factors that are larger, the better, they are converted through the following relationship:

[0141]

[0142] For the influencing factors that are smaller, the better, they are converted through the following relationship:

[0143]

[0144] In the formula, (r ij ) max represents the maximum value of the i-th influencing factor in the m-th sample; (r ij ) min Indicates the minimum value of the i-th influencing factor in the n-th sample;

[0145] S2. Establish a fuzzy decision model and calculate the comprehensive weight coefficient value:

[0146] T=W×B

[0147] T=[t 1 ,t 2 ,···,t m ] T ,

[0148] Among them, t i is the comprehensive weight coefficient value of each single well.

[0149] T=[0.3678,0.8430,0.2764] T

[0150] Step 5) taking the comprehensive weight coefficient value as the re-fracturing quality classification threshold; and using the quadrant analysis method to draw different re-fracturing effect areas according to the re-fracturing quality classification threshold and the initial monthly oil production of fracturing;

[0151] The comprehensive weight coefficient value of a single well is taken as the re-fracturing quality value. Based on the comprehensive weight coefficient values ​​of multiple single wells, the re-fracturing quality grading threshold between the second quadrant of II and the third quadrant of III, and between the first quadrant of I and the fourth quadrant of IV is determined to be 0.2. That is, the re-fracturing quality grading threshold ≥ 0.5 indicates that the re-fracturing quality is high, otherwise, the re-fracturing quality grading threshold < 0.5 indicates that the re-fracturing quality is poor.

[0152] Step 6) The second quadrant of II is selected first, followed by the third quadrant of III and the first quadrant of I, and the well layer to be repeatedly fractured is determined.

[0153] like Figure 4 As shown in the figure, based on the repeated fracturing quality grading threshold and the initial monthly oil production of fracturing, the quadrant analysis method is used to draw different repeated fracturing effect areas; according to the repeated fracturing quality grading threshold of 0.5 and the initial monthly oil production grading threshold of 160t / m, the quadrant analysis method is used to draw different repeated fracturing effect areas.

[0154] Determine the area with the best refracturing potential, and select the single well and layer for refracturing in this area.

[0155] That is, the second quadrant of II is given priority, followed by the third quadrant of III and the first quadrant of I. However, artificial measures are needed to improve the fracturing quality or oil production. The fourth quadrant of IV is not temporarily selected as the object of repeated fracturing.

[0156] The above examples are merely illustrative of the present invention and do not constitute a limitation on the protection scope of the present invention. All designs that are the same or similar to the present invention fall within the protection scope of the present invention.

Claims

1. A method for selecting wells and layers for repeated fracturing of ultra-low permeability reservoirs based on the integration of geology and engineering. Features: The following steps are involved: Step 1) establishing original data sets of geological factors, fracturing factors and production factors; Step 2) According to the established original data set, the original data set is standardized through range transformation, and then the correlation and weight value of the influencing factors are determined by using the grey correlation analysis method to screen the main control influencing parameters; Step 3) Use the analytic hierarchy process to construct a multi-parameter evaluation system hierarchy and determine the weight values ​​of the main control influencing parameters; Step 4) constructing a fuzzy comprehensive evaluation matrix based on geological factors, fracturing factors and production factors according to the fuzzy clustering method, combining the weight values ​​of the main control influencing parameters, and calculating the comprehensive weight coefficient value using the fuzzy decision model; Step 5) taking the comprehensive weight coefficient value as the re-fracturing quality classification threshold; and using the quadrant analysis method to draw different re-fracturing effect areas according to the re-fracturing quality classification threshold and the initial monthly oil production of fracturing; Step 6) The second quadrant of II is selected first, followed by the third quadrant of III and the first quadrant of I, and the well layer to be repeatedly fractured is determined.

2. The method for selecting wells and layers for repeated fracturing of ultra-low permeability reservoirs based on geological and engineering integration according to claim 1, Features: The geological factors in step 1) include porosity, permeability, oil saturation, effective thickness, length of oil-bearing sandstone, and remaining recoverable reserves.

3. The method for selecting wells and layers for repeated fracturing of ultra-low permeability reservoirs based on geological and engineering integration according to claim 1, Features: The fracturing factors in step 1) include fracturability, fracture half-length, proppant dosage, fracturing fluid dosage, sand addition amount, sand ratio, and displacement.

4. The method for selecting wells and layers for repeated fracturing of ultra-low permeability reservoirs based on geological and engineering integration according to claim 1, Features: The production factors in step 1) include formation pressure, water content, production pressure difference, recovery degree, and initial monthly oil production after fracturing.

5. The method for selecting wells and layers for repeated fracturing of ultra-low permeability reservoirs based on geological and engineering integration according to claim 1, Features: The specific process of screening the main control influencing parameters in step 2) is as follows: S1. Standardization of the original data of well and layer selection for repeated fracturing: The range transformation method is used to normalize the data into dimensionless values, and the parameters are standardized into positive or negative indicators: Positive indicators: Negative indicators: Where: S is the normalized value of the original data, dimensionless, and the value is between 0 and 1; X is the original data value; X min is the minimum value of the original data; X max is the maximum value of the original data; S2. Construct reference sequence and comparison sequence, and determine the correlation degree according to the grey correlation analysis method: The target is to increase the production of ultra-low permeability reservoir by fracturing. The original data set is the comparison sequence X i ={X i (k)|k=1,2,···,m|}(i=1,2,···,n), the initial monthly oil production of fracturing is the reference sequence X o ={X o (k)|k=1,2,···,m|}, determine the correlation coefficient between the comparison sequence and the reference sequence of different influencing factors; Among them, δ i (k) is the correlation between the comparison sequence and the reference sequence under different influencing factors; ρ is the resolution coefficient, ρ∈[0,1], the smaller the ρ value, the higher the resolution of the correlation degree, and the value range of ρ here is 0.4-0.6; |X o (k)-X i (k)| is the absolute difference between the reference sequence and the kth index in the i-th sequence; is the minimum absolute value of the sequence index; is the maximum absolute value of the sequence index; k is the number of wells, k = 1, 2, ..., m; i is the number of influencing factors, i = 1, 2, ..., n; S3. Determine the weight of the influencing factors according to the correlation, and select the main influencing parameters of the fracturing production increase effect: Calculate the correlation using the mean method: Among them, C i is the weight value of influencing factor i; r i is the correlation of influencing factor i; According to the weight values ​​of the influencing factors, the influencing factors with weight values ​​< 0.05 are removed, and the influencing factors with weight values ​​>= 0.05 are determined as the main control influencing parameters.

6. The method for selecting wells and layers for repeated fracturing of ultra-low permeability reservoirs based on geological and engineering integration according to claim 1, Features: The specific process of determining the weight value of the main control influencing parameter in step 3) is as follows: S1. Use the analytic hierarchy process to construct a hierarchical structure diagram for selecting wells and layers for repeated fracturing: The hierarchical structure diagram of well and layer selection for repeated fracturing includes target layer, criterion layer and scheme layer, with well and layer selection for repeated fracturing as the target layer, main control influencing parameters as the criterion layer and single well as the scheme layer; S2. According to the weight values ​​of the main control influencing parameters determined by the grey correlation analysis method, a judgment matrix A is constructed. ij ) n×n , a in the judgment matrix ij Indicates the importance of master control influencing parameter i relative to master control influencing parameter j, a ji Indicates the importance of the main control influencing parameter j relative to the main control influencing parameter i, and the importance of the main control influencing parameter is determined using the 1-9 scaling method; S3, use the geometric mean method to process the weight vector and maximum eigenvalue of the judgment matrix constructed in S2: Process the row vectors of the judgment matrix: Will Normalization processing: Determine the weight of each influencing parameter of this layer on the target layer: In=[In 1 ,IN 2 ,···,IN n ] T Where: W 1 , W 2 ,···,W n are the weight values ​​of different influencing parameters respectively; n is the number of parameters; The maximum eigenvalue of the judgment matrix is: S4. Perform consistency check on the judgment matrix: Among them, CI is the consistency index of the judgment matrix; CR is the random consistency ratio; RI is the consistency index; when CR < 0.10, the consistency of the judgment matrix is ​​considered reasonable; conversely, when CR ≥ 0.10, the judgment matrix needs to be appropriately adjusted until CR < 0.

10.

7. The method for selecting wells and layers for repeated fracturing of ultra-low permeability reservoirs based on geological and engineering integration according to claim 6, Features: The specific process of calculating the comprehensive weight coefficient value using the fuzzy decision model in step 4) is as follows: S1. Constructing the evaluation unit matrix of repeated fracturing well selection and layer selection R = (r ij ) n×m , and converted into a fuzzy comprehensive evaluation matrix B = (b ij ) n×m , where matrix R is the original data set, r ij is the original data value of the influencing factor of the well to be evaluated; n is the main influencing factor of the evaluation, m is the evaluation well layer; i = 1, 2, ..., n; j is the number of wells, j = 1, 2, ..., m; S2. Establish a fuzzy decision model and calculate the comprehensive weight coefficient value: T=W×B T=[t 1 ,t 2 ,···,t m ] T , Among them, t i is the comprehensive weight coefficient value of each single well.

8. The method for selecting wells and layers for repeated fracturing of ultra-low permeability reservoirs based on geological and engineering integration according to claim 7, Features: In S1, the evaluation unit matrix R = (r ij ) n×m Converted into fuzzy comprehensive evaluation matrix B = (b ij ) n×m When , the larger the better the influencing factors are, the following relationship is used for conversion: For the influencing factors that are smaller, the better, they are converted through the following relationship: In the formula, (r ij ) max represents the maximum value of the i-th influencing factor in the m-th sample; (r ij ) min Indicates the minimum value of the i-th influencing factor in the n-th sample.

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